{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "322e2367-8c5a-48d9-b491-436f8e6d96b0",
   "metadata": {},
   "source": [
    "# <span style=\"color: #9333EA;\">五  层叠柱形图</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "552ae72b-ab71-412c-8f6f-b40de5a0bd67",
   "metadata": {},
   "source": [
    "# 导入表格数据以及可视化\n",
    "### 筛选旗舰店的各季度的订单数据，分为已完成和已发货，生成一个层叠柱形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02075fee-b88d-4349-b490-bd93e376c2eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各季度订单数量统计：\n",
      "        已完成  已发货\n",
      "年份季度            \n",
      "2024Q4   22   25\n",
      "2025Q1   32   25\n",
      "2025Q2   28   21\n",
      "2025Q3   21   25\n",
      "2025Q4    4    2\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 解决中文显示问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 1. 读取并筛选旗舰店数据（替换为你的Excel路径）\n",
    "df = pd.read_excel(\"ERP订单数据.xlsx\", sheet_name=\"Sheet1\")\n",
    "flagship_df = df[df[\"店铺名称\"].str.contains(\"旗舰店\", na=False)].copy()\n",
    "\n",
    "# 2. 处理时间与季度\n",
    "flagship_df[\"下单时间\"] = pd.to_datetime(flagship_df[\"下单时间\"], errors=\"coerce\")\n",
    "flagship_df = flagship_df.dropna(subset=[\"下单时间\"])\n",
    "flagship_df[\"年份\"] = flagship_df[\"下单时间\"].dt.year\n",
    "flagship_df[\"季度\"] = flagship_df[\"下单时间\"].dt.quarter\n",
    "flagship_df[\"年份季度\"] = flagship_df[\"年份\"].astype(str) + \"Q\" + flagship_df[\"季度\"].astype(str)\n",
    "\n",
    "# 3. 定义目标季度范围\n",
    "target_quarters = [\"2024Q4\", \"2025Q1\", \"2025Q2\", \"2025Q3\", \"2025Q4\"]  # 仅保留有数据的季度\n",
    "\n",
    "# 4. 统计已完成、已发货订单数\n",
    "order_types = [\"已完成\", \"已发货\"]\n",
    "order_stats = {}\n",
    "for ot in order_types:\n",
    "    counts = flagship_df[flagship_df[\"订单状态\"] == ot][\"年份季度\"].value_counts().reindex(target_quarters, fill_value=0)\n",
    "    order_stats[ot] = counts\n",
    "\n",
    "# 5. 绘制并列柱形图\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "bar_width = 0.35  # 柱子宽度\n",
    "x = np.arange(len(target_quarters))  # 季度的x坐标\n",
    "\n",
    "# 颜色匹配参考图（蓝色：已完成；红色：已发货）\n",
    "colors = [\"#1f77b4\", \"#ff7f0e\"]\n",
    "\n",
    "# 绘制并列柱子\n",
    "for i, (ot, counts) in enumerate(order_stats.items()):\n",
    "    ax.bar(x + i * bar_width, counts.values, bar_width, label=ot, color=colors[i], alpha=0.8)\n",
    "\n",
    "# 6. 图表美化与标注\n",
    "ax.set_title(\"2024Q4-2025年各季度旗舰店订单数量\", fontsize=16, fontweight=\"bold\", pad=20)\n",
    "ax.set_xlabel(\"季度\", fontsize=12, fontweight=\"bold\")\n",
    "ax.set_ylabel(\"订单销量（单）\", fontsize=12, fontweight=\"bold\")\n",
    "\n",
    "# 设置x轴刻度与标签\n",
    "ax.set_xticks(x + bar_width/2)\n",
    "ax.set_xticklabels(target_quarters, rotation=45, ha=\"right\")\n",
    "\n",
    "# 添加网格线（仅纵轴）\n",
    "ax.yaxis.grid(True, alpha=0.3, linestyle=\"--\")\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "# 图例与标注（匹配参考图样式）\n",
    "ax.legend(loc=\"upper right\", fontsize=10, title=\"订单类型\", title_fontsize=12)\n",
    "\n",
    "# 给每个柱子标注数值\n",
    "for i, (ot, counts) in enumerate(order_stats.items()):\n",
    "    for j, val in enumerate(counts.values):\n",
    "        ax.text(x[j] + i * bar_width, val + 1, str(val), ha=\"center\", va=\"bottom\", fontsize=9)\n",
    "\n",
    "# 调整布局\n",
    "plt.tight_layout()\n",
    "\n",
    "# 保存并显示图表\n",
    "plt.savefig(\"旗舰店季度订单并列图.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "\n",
    "# 输出统计结果\n",
    "stats_df = pd.DataFrame(order_stats)\n",
    "print(\"各季度订单数量统计：\")\n",
    "print(stats_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0975da5e-95cd-4fda-8718-aa3f26ca21c4",
   "metadata": {},
   "source": [
    "一、订单总量趋势\r\n",
    "2024Q4 至 2025Q3 期间订单总量呈阶段性波动：\r\n",
    "2025Q1 达到峰值（共 57 单），\r\n",
    "2025Q4 显著回落（仅 6 这是因为表中Q4只有10月单），\r\n",
    "呈现 “先升后降” 的025Q2 后持续收缩。。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb5568f-ad85-4330-9d81-7b55c9250dab",
   "metadata": {},
   "source": [
    "二、订单类型结构\n",
    "1. 已完成订单\n",
    "峰值：2025Q1（32 单），\n",
    "低谷：2025Q4（4 单），\n",
    "整体随季度推移呈 “先升后降” 趋势，反映履约完成效率在 2025Q1 最优，后续逐渐下滑。\n",
    "2. 已发货订单\n",
    "稳定期：2024Q4、2025Q1、2025Q3 均为 25 单，\n",
    "下滑期：2025Q2 降至 21 单，2025Q4 进一步降至 2 单，发货环节订单量在 2025Q2 后持续收缩。"
   ]
  },
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   "execution_count": null,
   "id": "81d63e08-a483-4443-9550-74049c5ec6b6",
   "metadata": {},
   "outputs": [],
   "source": []
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